Big Data Analytics as a Service for Business Intelligence

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using B usiness intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords : Mobile business intelligence, business intelligence, big data, analytic

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

This paper examines big data and the opportunities it presents for improved business intelligence and decision making. Big data comes in multiple forms. It can be structured, semi-structured, or unstructured. The opportunity it presents is that there is so much of it and it is readily available to organizations. Organizations use big data for business intelligence (BI). They can apply analytics in BI activities to assess big data in order to gain new insights and opportunities for decision making. The problem is that oftentimes the data is of poor quality and it contains personal information. This paper explores these issues and examines the importance of effective data management in facilitating sound business intelligence. The Master Data Management methodology is reviewed and the importance of management support in its deployment is emphasized. With the advent of new sources of big data from IoT devices, the need for even more management involvement is stressed to ensure that organizational BI yield sound decisions and that use of data are in compliance with new regulations.

Recent advances in computing have lead to diverse applications in wide domains such as cyber-forensics, data science, business analytics, business intelligence, computer security, Web technology, and Big data analytics. Data analytics refer to broad term where many of the areas such as cyber-physical systems (CPS), Internet of Things (IoT), Big data, machine learning and data mining overlap among each other. However, there are subtle differences among these areas. Data analytics form as one of the key components of these wide areas in computing. For example, in IoT the data that are collected from various devices need to analyze for inferring the outcomes of it. Hence, data analytics need to be carried on it. There are various platforms available for data analytics. In this chapter, a brief overview of the term data analytics, different types of data, different types of analytics, and the analytical architecture is first discussed. It is later followed by the different phases that are involved in the lifecycle of the data analytics project and the interconnection of Big data and Hadoop ecosystem.

Purpose Textile and contemporary apparel manufacturers are adopting and integrating cutting-edge technologies to reduce their impact on the environment and gain an advantage in the marketplace. Most previous studies have ignored business intelligence systems (BIS), notably in the textile and apparel industry (T&A), in favor of looking at the larger picture of how big data would affect retail and distribution in a company. This is especially true for the T&As.Design/methodology/approach The authors report that they conducted 14 semi-structured interviews with 12 international luxury tourism service providers. In this case, researchers use snowball features and systematic techniques to select participants. A qualitative content analysis strategy is used to capture the focus of the interviews.Findings Problems with T&A company sustainability, opportunities to increase value creation via use of industry-leading business intelligence (BI) solutions and perceived roadblocks to BIS adoption were all found by the poll. Garment retail and distribution sector has benefited greatly from the increased use of Industry 4.0 technologies, especially those that provide better BI solutions. Determine the extent to which industry participation slows down or speeds up the process. The Company Information System (BIS) will help convince non-tech-savvy business owners of the financial, economic and environmental benefits of adopting certain technologies developed as part of the industry 4.0 movement.Research limitations/implications The authors of this research claim theirs is one of the first to investigate what variables affect the uptake of BIS, ultimately hoping to find out how BIS may be used by T&A businesses to tackle environmental issues through the use of Industry 4.0 technologies. The purpose of this study was to see whether BIS might aid T&A firms with their sustainability issues.Practical implications In the last several years, there has been a meteoric rise in interest in big data and business analytics among firms and educational institutions alike. This paper tries to introduce readers to the concept of business analytics in a way that is both academic and accessible, considering both the present and future of the field. This paper begins with a quick introduction, followed by a summary of the three distinct forms of predictive modeling discussed.Originality/value In an effort to help aspiring analytics professionals, they have identified, categorized and evaluated the nine distinct players that are now active in the analytics market. Following this, they will provide a high-level summary of the many different research projects currently being worked on by their group.

Competitive advantage is the ‘Holy Grail’ in strategic management theory. What makes a company more successful than its rivals has dominated scholarship in this area for more than 20 years. There have been two main theories proposed to attempt to identify the important resources and capabilities that configure to build competitive advantage; the Resource-based View and Dynamic Capability View. There is a growing literature stream in the area of Business Intelligence (BI) and Big Data Analytics with regard to both the computer technology and business management constructs. However, the literature is silent of the affordances of BI for Small to Medium Enterprises (SMEs), and so a significant gap in the literature remains. This discussion aims to signal the need to fill that gap and to build awareness of BI as a potentially significant contributor to sustained competitive advantage in SMEs underpinned by the iniquitousness of cloud applications previously the domain of Multinational Corporations.

In 2020, the Province of Bali succeeded in suppressing Covid-19 without Large-Scale Social Restrictions (PSBB), with the discipline of residents carrying out the prokes appeal. Various parties are reviewing the numbers up and down the spread of the Corona virus in order to anticipate a spike in cases. Big Data technology involves data that is diverse, rapidly changing, super large in size so that conventional technology is not able to handle it effectively. Big Data has a size, speed, variety that is too extreme to be managed by conventional techniques. Four stages of processing Big Data to produce output information for users, namely acquired, accessed, analytics, and application. Business Intelligence serves to transform operational, transactional data into useful information within the company. Big Data Analytics analyzes information, identifies it for current and past business decisions visualized in graphical form. The problem in this study is modeled in the Big Data architecture with the aim of mapping the trend of the spread of Covid-19 related to process regulation. From the distribution map data and regulation of health protocol rules with various online data sources, structured and unstructured using Business Intelligence (BI) data cleansing, three patterns of the spread of the corona virus were found in High Cases, Medium Cases and Low Cases from 34 provinces throughout Indonesia. West Java Province became the first order of the highest cases during May 2021 with an average case of 21% followed by DKI Jakarta 14%.

The purpose of the article is to determine the features of business intelligence systems, emphasizing their relationship with the professionalization of information analysts in the context of modern IT tools for the digitalization of society. Methodology of the research consists of the application of general scientific and special methods of cognition, in particular, methods of comparative analysis, terminological and other research methods, as well as methods of analytical Internet monitoring. The scientific novelty of this paper lies in the fact that the development trends are traced and the modern characteristics of business intelligence systems are determined in conjunction with the professional competencies of information analysts. The importance of the terminological aspect of defining business intelligence systems as "Business Intelligence" (BI) systems is emphasized. The interrelationships between information-analytical systems and businessanalytics systems at the content-functional level are revealed. Promising directions for the development ofbusiness intelligence systems in the structure of modern digital society are outlined. Conclusions. As a result of the study, it was revealed that modern trends in the development of business intelligence systems, taking into account foreign (American) experience, demonstrate the importance and necessity for the successful growth of enterprises and business structures the means of information and analytical activities integrated with information and communication technologies. Analytics and business analytics are seen not only as effective activities but also as a scientific approach to doing business. The recognition of the analytical profession as promising and in demand in the future is becoming obvious, as it is integrated into the information infrastructure of the economy and production in any sector of society. An important component of professional growth of the level of analysts is the improvement of knowledge and skills to work with information and communication technologies to effectively implement information-analytical and businessanalytical processes. Key words: business intelligence, business intelligence systems, information and analytical systems, business intelligence systems programs, information analytics.

Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

Intelligent big data analytics is an emerging paradigm for integrating big data, analytics, and artificial intelligence. The objective of this paper is to provide a managerial framework of intelligent big data analytics. More specifically, this paper proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service and management for improving business decision making. Then it elaborates intelligent big data analytics for management taking into account main managerial functions: planning, organising, leading and controlling. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, business intelligence, artificial intelligence and data science.

Big data is driving new forms of competition, new waves of productivity growth, innovation, and providing unique ways to interact with customers. Big data is comprised of a wealth of unstructured data that is being transmitted and captured by organizations to enhance business intelligence efforts. The forms that big data takes include text, video, voice, location data, social media, and its growth is exponential. Big data encompasses multiple dimensions, including volume, variety, velocity, veracity, variability, and complexity. Despite the opportunities that big data presents, organizations are having difficulty managing it, especially in the areas of data management and data governance. Executive involvement is critical to insuring quality data for business intelligence as well as to insure that their firms will be able to attract the necessary talent to effectively engage in BI analytics. It is recommended that management take a common sense approach to dealing with big data to insure that data-driven decision-making is not compromised.

Big data is driving new forms of competition, new waves of productivity growth, innovation, and providing unique ways to interact with customers. Big data is comprised of a wealth of unstructured data that is being transmitted and captured by organizations to enhance business intelligence efforts. The forms that big data takes include text, video, voice, location data, social media, and its growth is exponential. Big data encompasses multiple dimensions, including volume, variety, velocity, veracity, variability, and complexity. Despite the opportunities that big data presents, organizations are having difficulty managing it, especially in the areas of data management and data governance. Executive involvement is critical to ensuring quality data for business intelligence as well as to ensure that their firms will be able to attract the necessary talent to effectively engage in BI analytics. It is recommended that management take a common-sense approach to dealing with big data to ensure that data-driven decision making is not compromised.

In the era of big data, organizations are increasingly leveraging advanced analytics to extract valuable insights from vast and complex datasets. Management Information Systems (MIS) play a crucial role in collecting, processing, and analyzing data to support decision-making. Integrating big data analytics into MIS can enhance business intelligence and improve organizational performance. This study aims to investigate the integration of big data analytics into MIS and its impact on business intelligence. Specifically, the study seeks to identify the challenges and opportunities associated with this integration and explore best practices for implementation. A qualitative research approach was adopted for this study. Data was collected through semi-structured interviews based on a survey of over 312 information technology (IT) professionals from 21 industries with IT managers and business analysts from January 2022 to December 2023. Thematic analysis was used to analyze the data and identify key themes related to integrating big data analytics into MIS. The findings indicate that integrating big data analytics into MIS can significantly improve business intelligence. According to the respondents, on average, there was a 30% increase in the accuracy of decision-making processes after the integration. Additionally, organizations reported a 25% reduction in operational costs and a 20% increase in revenue as a result of the integration. Moreover, 70% of the respondents agreed that integrating big data analytics into MIS improved their organization's overall performance. Integrating big data analytics into MIS offers numerous benefits, including improved decision-making, cost savings, and revenue growth. However, organizations must overcome challenges such as data privacy and security concerns and the need for skilled personnel to manage and analyze big data. Overall, this study highlights the importance of integrating big data analytics into MIS for enhancing business intelligence and achieving organizational success.

The impact of business intelligence, big data analytics capability, and green knowledge management on sustainability performance